probabilis#c’ graphical’ models’ introduc#on’ mo#vaon’...
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Daphne Koller
predisposing factors symptoms test results
millions of pixels or thousands of superpixels
each needs to be labeled {grass, sky, water, cow, horse, …} diseases
treatment outcomes
Daphne Koller
Models Data
Model
Declarative representation
Algorithm Algorithm Algorithm
elicitation
domain expert
Learning
Daphne Koller
Uncertainty • Partial knowledge of state of the world
• Noisy observations
• Phenomena not covered by our model
• Inherent stochasticity
Daphne Koller
Probability Theory • Declarative representation with clear
semantics
• Powerful reasoning patterns
• Established learning methods
Daphne Koller
Complex Systems predisposing factors symptoms test results diseases treatment outcomes
class labels for thousands of superpixels
Random variables X1,…, Xn
Joint distribution P(X1,…, Xn)
Daphne Koller
Graphical Models
Intelligence Difficulty
Grade
Letter
SAT B D
C
A
Bayesian networks Markov networks
Daphne Koller
Graphical Representation • Intuitive & compact data structure • Efficient reasoning using general-purpose
algorithms • Sparse parameterization
– feasible elicitation – learning from data
Daphne Koller
Many Applications • Medical diagnosis • Fault diagnosis • Natural language
processing • Traffic analysis • Social network models • Message decoding
• Computer vision – Image segmentation – 3D reconstruction – Holistic scene analysis
• Speech recognition • Robot localization &
mapping
Daphne Koller
Textual Information Extraction Mrs. Green spoke today in New York. Green chairs the finance committee.
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Learned Model
Multi-Sensor Integration: Traffic • Trained on historical data • Learn to predict current & future road speed, including
on unmeasured roads • Dynamic route optimization
Multiple views on traffic
Incident reports
Weather
Thanks to: Eric Horvitz, Microsoft Research
• I95 corridor experiment: accurate to ±5 MPH in 85% of cases
• Fielded in 72 cities
Daphne Koller
Known 15/17 Supported 2/17 Reversed 1 Missed 3
Causal protein-signaling networks derived from multiparameter single-cell data Sachs et al., Science 2005
Biological Network Reconstruction Phospho-Proteins Phospho-Lipids Perturbed in data
PKC
Raf
Erk
Mek
Plcγ
PKA
Akt
Jnk P38
PIP2
PIP3
Subsequently validated in wetlab
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